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Machine Translation

Machine translation is the task of translating a sentence in a source language to a different target language.

Approaches for machine translation can range from rule-based to statistical to neural-based. More recently, encoder-decoder attention-based architectures like BERT have attained major improvements in machine translation.

One of the most popular datasets used to benchmark machine translation systems is the WMT family of datasets. Some of the most commonly used evaluation metrics for machine translation systems include BLEU, METEOR, NIST, and others.

( Image credit: Google seq2seq )

Papers

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Showing 7651–7700 of 10752 papers

TitleStatusHype
A Comprehensive Review on Recent Methods and Challenges of Video Description—0
A Comprehensive Survey of Grammar Error Correction—0
A Comprehensive Survey of Multilingual Neural Machine Translation—0
A Constrained Viterbi Relaxation for Bidirectional Word Alignment—0
A Context-aware Convolutional Natural Language Generation model for Dialogue Systems—0
A Context-Aware Topic Model for Statistical Machine Translation—0
A Contextual Language Model to Improve Machine Translation of Pronouns by Re-ranking Translation Hypotheses—0
A Continuous Improvement Framework of Machine Translation for Shipibo-Konibo—0
A Continuous Space Rule Selection Model for Syntax-based Statistical Machine Translation—0
A Continuum of Generation Tasks for Investigating Length Bias and Degenerate Repetition—0
A contrastive review of paraphrase acquisition techniques—0
A Conventional Orthography for Tunisian Arabic—0
A Convex Alternative to IBM Model 2—0
A Convexity-based Generalization of Viterbi for Non-Deterministic Weighted Automata—0
A Corpus and Semantic Parser for Multilingual Natural Language Querying of OpenStreetMap—0
A Corpus for English-Japanese Multimodal Neural Machine Translation with Comparable Sentences—0
A Corpus Level MIRA Tuning Strategy for Machine Translation—0
A corpus of German political speeches from the 21st century—0
A Corpus of Machine Translation Errors Extracted from Translation Students Exercises—0
A corpus of post-edited translations (Un corpus d'erreurs de traduction) [in French]—0
A Corpus of Preposition Supersenses—0
A Corpus of Spontaneous Speech in Lectures: The KIT Lecture Corpus for Spoken Language Processing and Translation—0
A Correlational Encoder Decoder Architecture for Pivot Based Sequence Generation—0
Acquiring distributed representations for verb-object pairs by using word2vec—0
Acquiring Knowledge from Pre-trained Model to Neural Machine Translation—0
Acquisition of Syntactic Simplification Rules for French—0
Acquisition of Translation Lexicons for Historically Unwritten Languages via Bridging Loanwords—0
A CRF Method of Identifying Prepositional Phrases in Chinese Patent Texts—0
A CRF Sequence Labeling Approach to Chinese Punctuation Prediction—0
ACRoBat: Optimizing Auto-batching of Dynamic Deep Learning at Compile Time—0
A Cross-lingual Messenger with Keyword Searchable Phrases for the Travel Domain—0
Action Classification and Highlighting in Videos—0
Active^2 Learning: Actively reducing redundancies in Active Learning methods for Sequence Tagging and Machine Translation—0
Active Learning and the Irish Treebank—0
Active Learning Approaches to Enhancing Neural Machine Translation—0
Active learning for interactive machine translation—0
Active Learning for Interactive Neural Machine Translation of Data Streams—0
Active Learning for Massively Parallel Translation of Constrained Text into Low Resource Languages—0
Active Learning for Post-Editing Based Incrementally Retrained MT—0
ACT-MNMT Auto-Constriction Turning for Multilingual Neural Machine Translation—0
Adabot: Fault-Tolerant Java Decompiler—0
AdaCoT: Rethinking Cross-Lingual Factual Reasoning through Adaptive Chain-of-Thought—0
Adam Induces Implicit Weight Sparsity in Rectifier Neural Networks—0
Adam Mickiewicz University at WMT 2022: NER-Assisted and Quality-Aware Neural Machine Translation—0
AdapNMT : Neural Machine Translation with Technical Domain Adaptation for Indic Languages—0
Adaptation and Combination of NMT Systems: The KIT Translation Systems for IWSLT 2016—0
Adaptation Data Selection using Neural Language Models: Experiments in Machine Translation—0
Adaptation incr\'ementale de mod\`eles de traduction neuronaux (Incremental adaptation of neural machine translation models)—0
Adaptation of a Rule-Based Translator to R\' de la Plata Spanish—0
Adaptation of Machine Translation Models with Back-translated Data using Transductive Data Selection Methods—0
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All datasetsWMT2014 English-GermanWMT2014 English-FrenchIWSLT2014 German-EnglishACESWMT2016 English-RomanianWMT2016 Romanian-EnglishWMT2014 German-EnglishIWSLT2015 German-EnglishWMT2016 English-GermanIWSLT2015 English-VietnameseIWSLT2015 English-GermanWMT2016 German-English

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Transformer Cycle (Rev)BLEU score35.14—Unverified
2Noisy back-translationBLEU score35—Unverified
3Transformer+Rep(Uni)BLEU score33.89—Unverified
4T5-11BBLEU score32.1—Unverified
5BiBERTBLEU score31.26—Unverified
6Transformer + R-DropBLEU score30.91—Unverified
7Bi-SimCutBLEU score30.78—Unverified
8BERT-fused NMTBLEU score30.75—Unverified
9Data Diversification - TransformerBLEU score30.7—Unverified
10SimCutBLEU score30.56—Unverified
#ModelMetricClaimedVerifiedStatus
1Transformer+BT (ADMIN init)BLEU score46.4—Unverified
2Noisy back-translationBLEU score45.6—Unverified
3mRASP+Fine-TuneBLEU score44.3—Unverified
4Transformer + R-DropBLEU score43.95—Unverified
5Transformer (ADMIN init)BLEU score43.8—Unverified
6AdminBLEU score43.8—Unverified
7BERT-fused NMTBLEU score43.78—Unverified
8MUSE(Paralllel Multi-scale Attention)BLEU score43.5—Unverified
9T5BLEU score43.4—Unverified
10Local Joint Self-attentionBLEU score43.3—Unverified
#ModelMetricClaimedVerifiedStatus
1PiNMTBLEU score40.43—Unverified
2BiBERTBLEU score38.61—Unverified
3Bi-SimCutBLEU score38.37—Unverified
4Cutoff + Relaxed Attention + LMBLEU score37.96—Unverified
5DRDABLEU score37.95—Unverified
6Transformer + R-Drop + CutoffBLEU score37.9—Unverified
7SimCutBLEU score37.81—Unverified
8Cutoff+KneeBLEU score37.78—Unverified
9CutoffBLEU score37.6—Unverified
10CipherDAugBLEU score37.53—Unverified
#ModelMetricClaimedVerifiedStatus
1HWTSC-Teacher-SimScore19.97—Unverified
2MS-COMET-22Score19.89—Unverified
3MS-COMET-QE-22Score19.76—Unverified
4KG-BERTScoreScore17.28—Unverified
5metricx_xl_DA_2019Score17.17—Unverified
6COMET-QEScore16.8—Unverified
7COMET-22Score16.31—Unverified
8UniTE-srcScore15.68—Unverified
9UniTE-refScore15.38—Unverified
10metricx_xxl_DA_2019Score15.24—Unverified